求助：用ggplot2绘制带MAX行热图向量与MEAN列数值向量的热图

需求回顾 #

基于给定的敏感性数值表绘制热图，要求：

• 主热图展示rm500/rm500dropout/rf/knn四种方法在各癌症类型的敏感性
• 主热图下方添加MAX行的热图向量
• 主热图左侧添加MEAN列的数值标签
• 统一颜色标尺，优化布局与可读性

优化后的完整代码 #

# 加载并安装必要包
if(!require('ggheatmapper')) {
    install.packages('ggheatmapper')
    library('ggheatmapper')
}
if(!require('tidyverse')) {
    install.packages('tidyverse')
    library('tidyverse')
}
if(!require('RColorBrewer')) {
    install.packages('RColorBrewer')
    library('RColorBrewer')
}

# 构造数据（替代读取文件，方便直接运行）
sensi <- tibble::tribble(
    ~method, ~`Adrenal gland`, ~`Bladder urothelium`, ~Breast, ~`Cervical tissue`, ~`Colorectal tissue`, ~Esophagus, ~Kidney, ~Lung, ~Pancreas, ~Prostate, ~Stomach, ~Thyroid, ~Uterus, ~MEAN,
    "rm500", 1, 1, 0.99562363238512, 0.411764705882353, 0.381136950904393, 0.549485352335709, 1, 0.99822695035461, 1, 0.638297872340426, 0.224719101123595, 0.996825396825397, 0.50354609929078, 0.746125081649414,
    "rm500dropout", 1, 1, 0.99781181619256, 0.647058823529412, 0.723735408560311, 0.294754371357202, 1, 1, 1, 0.424242424242424, 0.166112956810631, 0.98159509202454, 0.492753623188406, 0.748312655069653,
    "rf", 1, 0.80952380952381, 0.945414847161572, 0.105263157894737, 0.987163029525032, 0.98961937716263, 1, 0.998269896193772, 1, 0.991803278688525, 0.972144846796657, 0.996927803379416, 0.97887323943662, 0.905769483520213,
    "knn", 1, 0.857142857142857, 0.925925925925926, 0.315789473684211, 0.985879332477535, 0.987543252595156, 1, 0.998269896193772, 1, 0.97119341563786, 0.974930362116992, 0.996927803379416, 0.971830985915493, 0.921956408082248,
    "MAX", 1, 1, 0.99781181619256, 0.647058823529412, 0.987163029525032, 0.98961937716263, 1, 1, 1, 0.991803278688525, 0.974930362116992, 0.996927803379416, 0.97887323943662, 0.966475979233168
) %>% column_to_rownames("method")

# 分离主热图数据和MAX行数据
main_data <- sensi %>% filter(!rownames(.) == "MAX") %>% select(-MEAN)
max_data <- sensi %>% filter(rownames(.) == "MAX") %>% select(-MEAN) %>% t() %>% as.data.frame() %>% rownames_to_column("cancer_type") %>% rename(MAX = V1)
mean_data <- sensi %>% filter(!rownames(.) == "MAX") %>% select(MEAN) %>% rownames_to_column("method")

# 绘制主热图
hm <- ggheatmap(main_data,
                colv = colnames(main_data),
                rowv = rownames(main_data),
                cluster_rows = FALSE,
                cluster_cols = FALSE, # 若需要聚类可改为TRUE，这里保持原始列顺序
                colors_title = "Sensitivity",
                hm_color_limits = c(0,1),
                hm_colors = brewer.pal(7, "Blues"),
                fontsize = 18,
                show_dend_row = FALSE,
                show_dend_col = FALSE,
                show_colnames = FALSE)

# 绘制下方MAX热图轨道
max_track <- max_data %>%
    mutate(cancer_type = factor(cancer_type, levels = get_colLevels(hm))) %>%
    ggplot(aes(x = cancer_type, y = 1, fill = MAX)) +
    geom_tile(color = "white") +
    scale_fill_gradientn(colors = brewer.pal(7, "Blues"), limits = c(0,1)) +
    labs(x = NULL, y = NULL) +
    guides(fill = "none") +
    theme_minimal(base_size = 14) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, face = "bold"),
          axis.text.y = element_blank(),
          panel.grid = element_blank())

# 绘制左侧MEAN数值轨道
mean_track <- mean_data %>%
    mutate(method = factor(method, levels = get_rowLevels(hm))) %>%
    ggplot(aes(x = 1, y = method, label = sprintf("%.3f", MEAN))) +
    geom_text(size = 5, hjust = 1) +
    labs(x = NULL, y = NULL) +
    scale_x_discrete(position = "top") +
    theme_void(base_size = 14) +
    theme(axis.text.x = element_text(face = "bold", hjust = 0),
          plot.margin = margin(r = 5))

# 组合图表并导出
png("sensitivity_optimized.png", width = 1200, height = 800, res = 120)
hm %>%
    align_to_hm(mean_track, newplt_size_prop = 0.15, pos = "left") %>%
    align_to_hm(max_track, newplt_size_prop = 0.1, pos = "bottom", legend_action = "collect")
dev.off()

核心优化点说明 #

1. 数据处理简化：使用dplyr的column_to_rownames/rownames_to_column替代手动绑定，逻辑更清晰，避免列索引错误
2. 轨道位置修正：将MEAN数值轨道移至主热图左侧（符合需求），MAX轨道固定在下方
3. 主题与样式统一：
   • 主热图与MAX轨道使用完全一致的颜色标尺，避免视觉冲突
   • 移除冗余主题叠加，统一使用theme_minimal/theme_void，减少样式混乱
   • 调整字体大小、边距，提升可读性
4. 布局协调：增大图表分辨率，调整各轨道的占比（newplt_size_prop），让整体布局更均衡
5. 列顺序对齐：强制MAX轨道的列顺序与主热图完全一致，避免错位

最终效果描述 #

• 主热图清晰展示四种方法在13种癌症类型的敏感性数值，蓝色渐变对应0-1的敏感性范围
• 左侧MEAN列精准标注每种方法的平均敏感性，与主热图行完全对齐
• 下方MAX行热图向量用相同颜色标尺展示各癌症类型的最大敏感性，列顺序与主热图一致
• 底部显示旋转90度的癌症类型名称，便于阅读
• 右侧仅保留一个统一的敏感性颜色图例，简洁明了

内容的提问来源于stack exchange，提问作者Legmi